The long-term goal of this project is to build innovative informatics tools, capable of automatically querying and organizing phenotypic data (traits, syndromes, etc.) across Phenotype databases, to facilitate phenotypic research that aims to unlock the gene-disease-relationships. While genomic research at the molecular level (genes and proteins) has been intensively pursued, little progress has been made in functional genomics research, especially phenotypic research. However, there will soon be a pressing demand for greater integration of medicine and biology, and for related innovative informatics tools to facilitate research requiring phenotypic data. One limiting factor that significantly hinders the progress of phenotypic research is the lack of timely access to relevant phenotypes across databases. This application proposes to adopt a multidisciplinary approach (informatics, genomics and biomedical research) to explore the value of semantic, probabilistic and terminological technologies in phenotypic data and knowledge processing. The project focuses on the research and development of the Phenotype Organizer System (POS), a system designed to automate the processes of integration, organization and visualization of phenotypic data and knowledge. The hypotheses to be explored in this project are 1) a Phenotype Organizer System can be developed to effectively handle phenotypic data and knowledge from cancer and Model Organism System Databases, and 2) POS, by using the contextual knowledge of term usage (semantic, terminological and probabilistic) can improve the incorporation and organization of phenotypic information from distinct databases. Specific aims to test these hypotheses are: (i) to model and map the terminological components of various phenotypic databases using extended semantic networks; (ii) to create a knowledge base containing the contextual properties of the terminological components; and (iii) to develop the Web-enabled Phenotype Organizer System based on aims (i) and (ii). The proposed research may provide a unique approach to accelerate biomedical research by improving access to phenotypic data and knowledge processing - the Semantic Phenome (phenotypic-genomic relations). Further, results from these studies may yield innovative biomedical informatics tools that could be highly valuable for interdisciplinary mining of genomic, proteomic and clinical databases.